Volumetric datasets are found in many fields, such as engineering, material sciences, medical imaging, astrophysics and more recently as volumetric virtual reality for entertainment. The exploration and manipulation of volumetric datasets is not trivial, often requiring extensive knowledge and is usually heavily impacted by the specific needs of users and is finding applications in a growing number of fields, for example in the medical and security professions as well as in the entertainment domain for games, virtual reality post production and user-based interaction. In the security domain, for example, in most airports security agents deal with such data exploration in the context of baggage inspections. Conventional X-ray imaging and tomography techniques based on imaging by sections or sectioning, through the use of any kind of penetrating wave, including x rays, acoustic waves, light (in thermo-acoustic tomography for example) an the like, are two commonly used fluoroscopic scanning systems. Conventional X-ray systems provide a flattened 2D scan while tomography systems produce transversal scans, also called slices. Thanks to data processing techniques such as the Radon transform, these systems can produce a full 3D scan, comprising a set of voxels with corresponding density data. Since the resulting X-ray scanned image only contains voxel or pixel densities, they cannot display the original material colours. The current standard colour visual mapping uses three different colours (orange, green, and blue) to display the data density. Orange colour corresponds to low density (mainly organic items). In opposition, blue colour is used for high density values (i.e. metal). In the case of X-ray systems, green colour corresponds to the superposition of different kinds of materials or average density materials. Images generated in this manner can be valuable in many fields, including for example luggage scanning, study of human body, combustion in motors, silicon chips, advanced materials, and so on.
FIG. 1 demonstrates, for the example of baggage inspection, some of the ways in which an article may be obscured in a scan. As shown in FIG. 1, the displayed 2D scanned image can suffer from four main issues:
Superposition:
A threat (e.g. prohibited object such as a bottle, a knife, cutter or the like) may be sheltered behind dense materials. Sometimes, it is possible “to see” through this blind shield using functionalities such as high penetration (enhanced X-ray power) or image processing (contrast improvement). As shown in FIG. 1, the umbrella and dense collection of objects in the upper right hand corner 101 may obscure articles of interest.
Location:
Depending on its location inside the luggage, a threat can be difficult to detect. Objects located in the corners, in the edges or inside the luggage's frame are very difficult to identify. As shown in FIG. 1, the retractable trolley bars and the rigid corners of the case 102 may obscure articles of interest.
Dissociation:
Another way to dissimulate a threat is to separate and to spread parts of it in the luggage (weapons or explosives are composed of many separated items like the trigger, the barrel . . . ). This dissociation can be combined with other dissimulation techniques. As shown in FIG. 1, a number of apparently non-descript items 103 are present which are unlikely to attract particular attention, but which may be assembled to form some article of interest.
Lure:
An ill-intentioned individual may use a lure to hide the real threat. For instance, a minor threat like a small pair of scissors may be clearly visible and catch a security agent's attention while a more important threat remains hidden. As shown in FIG. 1, the metal rod 104 may attract the attention of the user, drawing it away from some less visible threat.
Volumetric data exploration with direct volume rendering techniques is of great help to visually extract relevant structures in many fields of science: medical imaging, astrophysics and more recently in luggage security. To leverage this knowledge extraction, many techniques have been developed. A number of existing basic technologies are known in this field, including volume visualization, transfer function, direct voxel manipulation and focus plus context interaction.
In particular, volume visualization can be done with geometric rendering systems which transform the data into a set of polygons representing an iso-surface. The contour tree algorithm and other alternatives such as branch decomposition are usually used to find these iso-surfaces. Contour tree algorithms may be vulnerable to noise, which can be problematic for instance in luggage inspections since dense materials such as steel cause noise by reflecting the X-rays.
In order to investigate volumetric dataset, one can use the Transfer Function (TF). In practice, this maps the voxel density with a specific colour (including its transparency). Transfer functions can be 1, 2 or n dimensional and are of great help to isolate structures of interest in volumetric data. Thanks to the colour blending process, a suitable transfer function can also reveal iso-surfaces or hide density to improve the volumetric data visualization.
FIG. 2 illustrates a difficulty arising in the context of FIG. 1.
A specific difficulty that arises in an environment such as that described with respect to FIG. 1 is that the user's view, which may be defined for example as shown in terms of a virtual viewpoint 200 having a specified position, axis of orientation 201 and field of view θ 202, of a particular article or region of interest such as the article 210 will often be obscured by materials of less or no interest such as the article 220. In order to better view the object or region of interest, the user may wish disregard certain such materials so as to achieve an improved view.
There is thus a need of mechanisms supporting the generation of such improved views.